E-W structure using null models
First, read the csv file with the list of number of individuals per OTU in each mountain. | Primero, se lee el documento csv con la lista de individuos de cada OTU por montaña
OTUs <- read.csv("OTUs.csv")
row.names(OTUs)<-OTUs$OTU
OTUs<-OTUs[1:44,2:8] #delete column OTU, not needed.
OTUsConvert the csv file to matrix and tranpose the matrix so that otus are in columns and sites in rows. Then, calculate a presence-absence matrix, where each cell will be first converted to T if there are one or more individuals in a site, and F if there are 0 individuals. Then, T are transformed to 1, and F to 0. | Se traspone la matriz para que las especies sean las columnas (variables) y los sitios los renglones. DespuĂ©s, se calcula una matriz de presencia-ausencia, en donde cada celda es transformada en T si hay al menos un individuo o mĂ¡s en el sitio, y F si hay 0 individuos. DespuĂ©s se transforman las T en 1 y las F en 0.
OTUs<-as.matrix(OTUs) #La convertimos en matriz
OTUs<-t(OTUs) #Trasponemos la matriz para que las especies sean las columnas (variables) y los sitios los renglones.
image(t(log(OTUs+1)), axes=FALSE, ylab="sites", xlab="OTU") #Se ve en la imagen los valores de abundancia, los colores rojos indican abundancias bajas, amarillo a blancos indica abundancias altas.
OTUs.PA<-OTUs>0 #T and F instead of number of individuals
mode(OTUs.PA)<-"integer" # T=1 y F=0
head(OTUs)## 1 2 3 4 5 6 7 8 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
## An 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4
## Bl 0 0 0 0 0 0 0 4 5 0 0 0 0 0 1 0 1 0 3 0 0 0 0 0 0 1
## To 5 0 0 0 0 0 0 5 14 0 0 0 0 0 0 0 9 0 4 2 0 0 0 0 0 0
## Aj 0 0 0 0 0 1 0 1 0 0 2 11 6 8 0 0 2 0 1 0 2 2 4 4 5 0
## Iz 0 0 10 2 0 0 0 0 0 6 0 0 7 0 0 0 0 0 3 1 0 0 0 0 3 0
## Ma 0 4 3 0 0 0 1 4 0 8 0 0 0 0 0 0 2 1 0 0 0 0 0 0 0 0
## 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45
## An 0 0 0 0 0 0 1 0 0 0 0 0 0 2 3 0 0 3
## Bl 0 0 0 0 0 0 0 1 1 0 0 0 2 0 0 0 0 0
## To 0 0 0 0 0 0 0 0 0 0 1 0 3 0 0 2 2 0
## Aj 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0
## Iz 5 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0
## Ma 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0OTUs.PA## 1 2 3 4 5 6 7 8 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
## An 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## Bl 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 1
## To 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0
## Aj 0 0 0 0 0 1 0 1 0 0 1 1 1 1 0 0 1 0 1 0 1 1 1 1 1 0
## Iz 0 0 1 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 1 0 0 0 0 1 0
## Ma 0 1 1 0 0 0 1 1 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0
## Pe 0 0 1 0 1 0 0 0 0 1 0 0 0 1 1 1 0 1 0 0 0 0 0 1 0 0
## 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45
## An 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 1
## Bl 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0
## To 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 1 0
## Aj 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0
## Iz 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## Ma 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## Pe 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0In a null model, empirical data are compared to simulated data using the function oecosimu. The presence-absence matrix (explained in diversity.Rmd) is used to calculate beta diversity. I made two groups representing eastern and western mountains. Since there are 7 mountains, 34 combinations of eastern-western mountains different than the real one can exist. After making two groups of mountains in 34 possible combinations, data are randomized between the two groups. This way, 34 randomized groups are compared with the actual E-W structure we are testing. | Se comparan los datos empĂricos con datos simulados con la funciĂ³n oecosimu. Se usa la base de datos de presencia-ausencia construida para calcular la diversidad beta. Hice dos grupos que representan montañas del Este y montañas del Oeste. Debido a que hay 7 montañas, existen 34 posibles combinaciones de agrupamientos E-O. DespuĂ©s de hacer dos grupos de montañas con las 34 posibles combinaciones, los datos fueron aleatorizados entre los dos grupos. De esta manera, los 34 grupos aleatorios son comĂ¡rados con el agrupamiento que representa la estructura E-O que se estĂ¡ poniendo a prueba.
library(vegan)## Warning: package 'vegan' was built under R version 3.5.2## Loading required package: permute## Warning: package 'permute' was built under R version 3.5.2## Loading required package: lattice## This is vegan 2.5-6#Group1: E=An,Bl,To W=Aj,Iz,Ma,Pe
E1<-OTUs.PA[1:3,] # vector E
E1<-colSums(E1) #Add all species from these 3 mountains
W1<-OTUs.PA[4:7,] #vector W
W1<-colSums(W1)
EW1<-matrix(data=(c(E1,W1)),nrow=2,ncol=44,byrow=T,dimnames=list(c("E1","W1"))) #Build matrix E-W
EW1## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
## E1 1 0 0 0 0 0 0 2 2 0 0 0 0
## W1 0 1 3 1 1 1 1 2 0 3 1 1 2
## [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25]
## E1 0 1 0 2 0 2 1 0 0 0 0 0
## W1 2 1 1 2 2 2 1 1 1 1 2 2
## [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36] [,37]
## E1 2 0 0 0 0 0 0 1 1 1 0 1
## W1 0 2 1 1 1 1 1 0 0 0 1 0
## [,38] [,39] [,40] [,41] [,42] [,43] [,44]
## E1 0 2 1 1 1 1 1
## W1 1 1 0 0 0 0 0sim1st<-oecosimu(EW1,nestedbetasor,method="r0",nsimul=999) #randomize sites## Warning in oecosimu(EW1, nestedbetasor, method = "r0", nsimul = 999):
## nullmodel transformed 'comm' to binary datasim1st## oecosimu object
##
## Call: oecosimu(comm = EW1, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.666667 4.9667 0.27183 0.11111 0.27778 0.4444 0.001 ***
## nestedness 0.093333 -4.9667 0.20389 0.15556 0.20222 0.2489 0.001 ***
## sorensen 0.760000 4.9667 0.47572 0.36000 0.48000 0.6000 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1c(sim1st$statistic[1:3], sim1st$oecosimu$pval[1:3]) # get diversity statistics and their pvalue## turnover nestedness sorensen
## 0.66666667 0.09333333 0.76000000 0.00100000 0.00100000 0.00100000#plot
dplot1st<-densityplot(permustats(sim1st))
dplot1st
Repeat with all 35 combinations | Se repite el mismo procedimiento con los 35 agrupamientos de montañas.
## Read file with the 35 groupings
EW_groups<-read.delim("EW_groups.txt", stringsAsFactors = FALSE)
## Perform analysis in a loop
div_results<-as.numeric() # to save div results
for(i in c(1:35)){
# get character vector of group W and E
W<-strsplit(EW_groups[i,2], ",")[[1]]
print(paste("results for grouping", i , "W:"))
print(W)
E<-strsplit(EW_groups[i,3], ",")[[1]]
print("and E:")
print(E)
# Get matrix for group i
W<-OTUs.PA[W,]
E<-OTUs.PA[E,]
W<-colSums(W)
E<-colSums(E)
EW<-matrix(data=(c(E,W)),nrow=2,ncol=44,byrow=T,dimnames=list(c("E","W")))
# Run analysis
sim1st<-oecosimu(EW,nestedbetasor,method="r0",nsimul=999) #randomize sites
print(sim1st)
x<-c(sim1st$statistic[1:3], sim1st$oecosimu$pval[1:3]) # get diversity statistics and their pvalue
div_results<-rbind(div_results,x) # save in table outside loop
# #plot
dplot1st<-densityplot(permustats(sim1st))
print(dplot1st)
}## [1] "results for grouping 1 W:"
## [1] "An" "Bl" "To"
## [1] "and E:"
## [1] "Aj" "Iz" "Ma" "Pe"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.666667 4.8748 0.26827 0.11111 0.27778 0.4444 0.001 ***
## nestedness 0.093333 -4.8748 0.20488 0.15556 0.20222 0.2489 0.001 ***
## sorensen 0.760000 4.8748 0.47315 0.36000 0.48000 0.6000 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "results for grouping 2 W:"
## [1] "An" "Bl" "Aj"
## [1] "and E:"
## [1] "To" "Iz" "Ma" "Pe"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.576923 3.9761 0.339686 0.230769 0.346154 0.4615 0.001
## nestedness 0.023077 -3.9761 0.036017 0.029371 0.035664 0.0420 0.001
## sorensen 0.600000 3.9761 0.375703 0.272727 0.381818 0.4909 0.001
##
## turnover ***
## nestedness ***
## sorensen ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "results for grouping 3 W:"
## [1] "An" "Bl" "Iz"
## [1] "and E:"
## [1] "To" "Aj" "Ma" "Pe"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.40000 3.5103 0.18193 0.05000 0.20000 0.3000 0.003 **
## nestedness 0.17143 -3.5103 0.23373 0.20000 0.22857 0.2714 0.003 **
## sorensen 0.57143 3.5103 0.41567 0.32143 0.42857 0.5000 0.003 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "results for grouping 4 W:"
## [1] "An" "Bl" "Ma"
## [1] "and E:"
## [1] "To" "Aj" "Iz" "Pe"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.50000 3.8873 0.205817 0.055556 0.222222 0.3333 0.001
## nestedness 0.16038 -3.8873 0.254738 0.213836 0.249476 0.3029 0.001
## sorensen 0.66038 3.8873 0.460555 0.358491 0.471698 0.5472 0.001
##
## turnover ***
## nestedness ***
## sorensen ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "results for grouping 5 W:"
## [1] "An" "Bl" "Pe"
## [1] "and E:"
## [1] "To" "Aj" "Iz" "Ma"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.545455 4.1234 0.27286 0.13636 0.27273 0.4091 0.001 ***
## nestedness 0.084175 -4.1234 0.13465 0.10943 0.13468 0.1599 0.001 ***
## sorensen 0.629630 4.1234 0.40752 0.29630 0.40741 0.5185 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "results for grouping 6 W:"
## [1] "An" "To" "Aj"
## [1] "and E:"
## [1] "Bl" "Iz" "Ma" "Pe"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.520000 3.7599 0.293013 0.160000 0.280000 0.4000 0.001
## nestedness 0.051429 -3.7599 0.075749 0.064286 0.077143 0.0900 0.001
## sorensen 0.571429 3.7599 0.368762 0.250000 0.357143 0.4643 0.001
##
## turnover ***
## nestedness ***
## sorensen ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "results for grouping 7 W:"
## [1] "An" "To" "Iz"
## [1] "and E:"
## [1] "Bl" "Aj" "Ma" "Pe"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.47619 3.7665 0.228705 0.095238 0.238095 0.3810 0.001
## nestedness 0.12381 -3.7665 0.182306 0.146320 0.180087 0.2139 0.001
## sorensen 0.60000 3.7665 0.411011 0.309091 0.418182 0.5273 0.001
##
## turnover ***
## nestedness ***
## sorensen ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "results for grouping 8 W:"
## [1] "An" "To" "Ma"
## [1] "and E:"
## [1] "Bl" "Aj" "Iz" "Pe"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.50000 3.9373 0.22758 0.10000 0.25000 0.3500 0.001 ***
## nestedness 0.12963 -3.9373 0.20026 0.16852 0.19444 0.2333 0.001 ***
## sorensen 0.62963 3.9373 0.42784 0.33333 0.44444 0.5185 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "results for grouping 9 W:"
## [1] "An" "To" "Pe"
## [1] "and E:"
## [1] "Bl" "Aj" "Iz" "Ma"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.48000 3.5499 0.271271 0.160000 0.280000 0.4000 0.001
## nestedness 0.06386 -3.5499 0.089493 0.073684 0.088421 0.1032 0.001
## sorensen 0.54386 3.5499 0.360764 0.263158 0.368421 0.4737 0.001
##
## turnover ***
## nestedness ***
## sorensen ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "results for grouping 10 W:"
## [1] "An" "Aj" "Iz"
## [1] "and E:"
## [1] "Bl" "To" "Ma" "Pe"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.60000 4.044 0.341181 0.240000 0.320000 0.4800 0.001 ***
## nestedness 0.02963 -4.044 0.048801 0.038519 0.050370 0.0563 0.001 ***
## sorensen 0.62963 4.044 0.389983 0.296296 0.370370 0.5185 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "results for grouping 11 W:"
## [1] "An" "Aj" "Ma"
## [1] "and E:"
## [1] "Bl" "To" "Iz" "Pe"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.555556 3.6418 0.341082 0.222222 0.333333 0.4444 0.007 **
## nestedness 0.015873 -3.6418 0.023533 0.019841 0.023810 0.0278 0.007 **
## sorensen 0.571429 3.6418 0.364615 0.250000 0.357143 0.4643 0.007 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "results for grouping 12 W:"
## [1] "An" "Aj" "Pe"
## [1] "and E:"
## [1] "Bl" "To" "Iz" "Ma"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.521739 3.7978 0.270314 0.130435 0.260870 0.3913 0.001
## nestedness 0.078261 -3.7978 0.119403 0.099605 0.120949 0.1423 0.001
## sorensen 0.600000 3.7978 0.389717 0.272727 0.381818 0.4909 0.001
##
## turnover ***
## nestedness ***
## sorensen ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "results for grouping 13 W:"
## [1] "An" "Iz" "Ma"
## [1] "and E:"
## [1] "Bl" "To" "Aj" "Pe"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.36842 3.2496 0.162531 0.052632 0.157895 0.3158 0.003 **
## nestedness 0.20301 -3.2496 0.269186 0.219925 0.270677 0.3045 0.003 **
## sorensen 0.57143 3.2496 0.431717 0.357143 0.428571 0.5357 0.003 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "results for grouping 14 W:"
## [1] "An" "Iz" "Pe"
## [1] "and E:"
## [1] "Bl" "To" "Aj" "Ma"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.40909 3.2998 0.204341 0.090909 0.181818 0.3182 0.001
## nestedness 0.13477 -3.2998 0.181466 0.155502 0.186603 0.2073 0.001
## sorensen 0.54386 3.2998 0.385807 0.298246 0.368421 0.4737 0.001
##
## turnover ***
## nestedness ***
## sorensen ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "results for grouping 15 W:"
## [1] "An" "Ma" "Pe"
## [1] "and E:"
## [1] "Bl" "To" "Aj" "Iz"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.57895 4.3449 0.24746 0.10526 0.26316 0.4211 0.001 ***
## nestedness 0.11336 -4.3449 0.20261 0.15587 0.19838 0.2409 0.001 ***
## sorensen 0.69231 4.3449 0.45007 0.34615 0.46154 0.5769 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "results for grouping 16 W:"
## [1] "Bl" "To" "Aj"
## [1] "and E:"
## [1] "An" "Iz" "Ma" "Pe"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.538462 3.7957 0.318934 0.192308 0.307692 0.4231 0.001
## nestedness 0.032967 -3.7957 0.048648 0.041209 0.049451 0.0577 0.001
## sorensen 0.571429 3.7957 0.367582 0.250000 0.357143 0.4643 0.001
##
## turnover ***
## nestedness ***
## sorensen ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "results for grouping 17 W:"
## [1] "Bl" "To" "Iz"
## [1] "and E:"
## [1] "An" "Aj" "Ma" "Pe"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.50000 3.9188 0.22432 0.10000 0.25000 0.3500 0.001 ***
## nestedness 0.12963 -3.9188 0.20110 0.16852 0.19444 0.2333 0.001 ***
## sorensen 0.62963 3.9188 0.42543 0.33333 0.44444 0.5185 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "results for grouping 18 W:"
## [1] "Bl" "To" "Ma"
## [1] "and E:"
## [1] "An" "Aj" "Iz" "Pe"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.47368 3.8363 0.206154 0.052632 0.210526 0.3158 0.001
## nestedness 0.15595 -3.8363 0.235214 0.202729 0.233918 0.2807 0.001
## sorensen 0.62963 3.8363 0.441367 0.333333 0.444444 0.5185 0.001
##
## turnover ***
## nestedness ***
## sorensen ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "results for grouping 19 W:"
## [1] "Bl" "To" "Pe"
## [1] "and E:"
## [1] "An" "Aj" "Iz" "Ma"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.521739 3.8838 0.270401 0.130435 0.260870 0.3913 0.001
## nestedness 0.078261 -3.8838 0.119389 0.099605 0.120949 0.1423 0.001
## sorensen 0.600000 3.8838 0.389790 0.272727 0.381818 0.4909 0.001
##
## turnover ***
## nestedness ***
## sorensen ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "results for grouping 20 W:"
## [1] "Bl" "Aj" "Iz"
## [1] "and E:"
## [1] "An" "To" "Ma" "Pe"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.555556 3.7567 0.341564 0.222222 0.333333 0.4444 0.001
## nestedness 0.015873 -3.7567 0.023516 0.019841 0.023810 0.0278 0.001
## sorensen 0.571429 3.7567 0.365079 0.250000 0.357143 0.4643 0.001
##
## turnover ***
## nestedness ***
## sorensen ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "results for grouping 21 W:"
## [1] "Bl" "Aj" "Ma"
## [1] "and E:"
## [1] "An" "To" "Iz" "Pe"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.4827586 3.314 0.3171447 0.2068966 0.3103448 0.4138 0.001
## nestedness 0.0087668 -3.314 0.0115738 0.0099357 0.0116891 0.0134 0.001
## sorensen 0.4915254 3.314 0.3287185 0.2203390 0.3220339 0.4237 0.001
##
## turnover ***
## nestedness ***
## sorensen ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "results for grouping 22 W:"
## [1] "Bl" "Aj" "Pe"
## [1] "and E:"
## [1] "An" "To" "Iz" "Ma"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.500000 3.769 0.271021 0.166667 0.291667 0.3750 0.001 ***
## nestedness 0.071429 -3.769 0.104140 0.089286 0.101190 0.1190 0.001 ***
## sorensen 0.571429 3.769 0.375161 0.285714 0.392857 0.4643 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "results for grouping 23 W:"
## [1] "Bl" "Iz" "Ma"
## [1] "and E:"
## [1] "An" "To" "Aj" "Pe"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.26316 2.7968 0.11364 0.00000 0.10526 0.2105 0.013 *
## nestedness 0.25408 -2.7968 0.30564 0.27223 0.30853 0.3448 0.013 *
## sorensen 0.51724 2.7968 0.41928 0.34483 0.41379 0.4828 0.013 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "results for grouping 24 W:"
## [1] "Bl" "Iz" "Pe"
## [1] "and E:"
## [1] "An" "To" "Aj" "Ma"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.34783 3.0292 0.183749 0.086957 0.173913 0.3043 0.007 **
## nestedness 0.14370 -3.0292 0.179852 0.153279 0.182019 0.2012 0.007 **
## sorensen 0.49153 3.0292 0.363601 0.288136 0.355932 0.4576 0.007 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "results for grouping 25 W:"
## [1] "Bl" "Ma" "Pe"
## [1] "and E:"
## [1] "An" "To" "Aj" "Iz"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.50000 3.9682 0.22678 0.10000 0.20000 0.3500 0.003 **
## nestedness 0.12963 -3.9682 0.20047 0.16852 0.20741 0.2333 0.003 **
## sorensen 0.62963 3.9682 0.42724 0.33333 0.40741 0.5185 0.003 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "results for grouping 26 W:"
## [1] "To" "Aj" "Iz"
## [1] "and E:"
## [1] "An" "Bl" "Ma" "Pe"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.625000 4.4427 0.341675 0.208333 0.333333 0.4583 0.001
## nestedness 0.035377 -4.4427 0.062106 0.051101 0.062893 0.0747 0.001
## sorensen 0.660377 4.4427 0.403781 0.283019 0.396226 0.5094 0.001
##
## turnover ***
## nestedness ***
## sorensen ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "results for grouping 27 W:"
## [1] "To" "Aj" "Ma"
## [1] "and E:"
## [1] "An" "Bl" "Iz" "Pe"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.48148 3.3126 0.295221 0.185185 0.296296 0.4074 0.005 **
## nestedness 0.03576 -3.3126 0.048605 0.040868 0.048531 0.0562 0.005 **
## sorensen 0.51724 3.3126 0.343827 0.241379 0.344828 0.4483 0.005 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "results for grouping 28 W:"
## [1] "To" "Aj" "Pe"
## [1] "and E:"
## [1] "An" "Bl" "Iz" "Ma"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.43478 3.3655 0.22758 0.13043 0.21739 0.3478 0.001 ***
## nestedness 0.10908 -3.3655 0.14906 0.12586 0.15103 0.1678 0.001 ***
## sorensen 0.54386 3.3655 0.37664 0.29825 0.36842 0.4737 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "results for grouping 29 W:"
## [1] "To" "Iz" "Ma"
## [1] "and E:"
## [1] "An" "Bl" "Aj" "Pe"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.42105 3.5428 0.182288 0.052632 0.157895 0.3158 0.001
## nestedness 0.17895 -3.5428 0.252747 0.211483 0.260287 0.2928 0.001
## sorensen 0.60000 3.5428 0.435035 0.345455 0.418182 0.5273 0.001
##
## turnover ***
## nestedness ***
## sorensen ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "results for grouping 30 W:"
## [1] "To" "Iz" "Pe"
## [1] "and E:"
## [1] "An" "Bl" "Aj" "Ma"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.41667 3.3074 0.22885 0.12500 0.20833 0.3333 0.005 **
## nestedness 0.10057 -3.3074 0.13296 0.11494 0.13649 0.1509 0.005 **
## sorensen 0.51724 3.3074 0.36181 0.27586 0.34483 0.4483 0.005 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "results for grouping 31 W:"
## [1] "To" "Ma" "Pe"
## [1] "and E:"
## [1] "An" "Bl" "Aj" "Iz"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.5 3.9193 0.24998 0.13636 0.22727 0.3636 0.001 ***
## nestedness 0.1 -3.9193 0.15000 0.12727 0.15455 0.1727 0.001 ***
## sorensen 0.6 3.9193 0.39998 0.30909 0.38182 0.4909 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "results for grouping 32 W:"
## [1] "Aj" "Iz" "Ma"
## [1] "and E:"
## [1] "An" "Bl" "To" "Pe"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.62963 4.2227 0.38527 0.25926 0.37037 0.4815 0.001 ***
## nestedness 0.00000 0.0000 0.00000 0.00000 0.00000 0.0000 1.000
## sorensen 0.62963 4.2227 0.38527 0.25926 0.37037 0.4815 0.001 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "results for grouping 33 W:"
## [1] "Aj" "Iz" "Pe"
## [1] "and E:"
## [1] "An" "Bl" "To" "Ma"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.608696 4.2024 0.319145 0.173913 0.304348 0.4348 0.001
## nestedness 0.051682 -4.2024 0.089924 0.074651 0.091879 0.1091 0.001
## sorensen 0.660377 4.2024 0.409069 0.283019 0.396226 0.5094 0.001
##
## turnover ***
## nestedness ***
## sorensen ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "results for grouping 34 W:"
## [1] "Aj" "Ma" "Pe"
## [1] "and E:"
## [1] "An" "Bl" "To" "Iz"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.625000 4.4182 0.337504 0.208333 0.333333 0.4583 0.001
## nestedness 0.035377 -4.4182 0.062500 0.051101 0.062893 0.0747 0.001
## sorensen 0.660377 4.4182 0.400004 0.283019 0.396226 0.5094 0.001
##
## turnover ***
## nestedness ***
## sorensen ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "results for grouping 35 W:"
## [1] "Iz" "Ma" "Pe"
## [1] "and E:"
## [1] "An" "Bl" "To" "Aj"
## oecosimu object
##
## Call: oecosimu(comm = EW, nestfun = nestedbetasor, method = "r0",
## nsimul = 999)
##
## nullmodel method 'r0' with 999 simulations
##
## alternative hypothesis: statistic is less or greater than simulated values
##
## statistic SES mean 2.5% 50% 97.5% Pr(sim.)
## turnover 0.47619 3.8401 0.227847 0.095238 0.238095 0.3333 0.001
## nestedness 0.12381 -3.8401 0.182509 0.157576 0.180087 0.2139 0.001
## sorensen 0.60000 3.8401 0.410356 0.309091 0.418182 0.4909 0.001
##
## turnover ***
## nestedness ***
## sorensen ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
colnames(div_results)[4:6]<-c("p_turnoever", "p_nestedness", "p_sorensen")
rownames(div_results)<-NULL
div_results<-round(div_results[,],4)
EW_results_null_model<-cbind(EW_groups, div_results)
EW_results_null_modelPerform an analysis of variance with permultations | AnĂ¡lisis de varianza con permutaciones
permu_results<-numeric()
for(i in 1:35){
# get character vector of group W and E
W<-strsplit(EW_groups[i,2], ",")[[1]]
print(paste("results for grouping", i , "W:"))
print(W)
E<-strsplit(EW_groups[i,3], ",")[[1]]
print("and E:")
print(E)
## Generate EW vector for each grouping
vec<-c(W, E)
vec<-sub("An|Bl|To", "W", vec)
vec<-sub("Aj|Iz|Ma|Pe", "E", vec)
## run permutation analyses
set.seed(100) #set seed to get same results every run
OTUsdist<-betadiver(OTUs.PA,method=1)
AV<-adonis(OTUsdist~as.factor(vec),permutation=999)
AV
permu_results<-rbind(permu_results, AV$aov.tab$Pr[1])
}## [1] "results for grouping 1 W:"
## [1] "An" "Bl" "To"
## [1] "and E:"
## [1] "Aj" "Iz" "Ma" "Pe"## Set of permutations < 'minperm'. Generating entire set.## [1] "results for grouping 2 W:"
## [1] "An" "Bl" "Aj"
## [1] "and E:"
## [1] "To" "Iz" "Ma" "Pe"## Set of permutations < 'minperm'. Generating entire set.## [1] "results for grouping 3 W:"
## [1] "An" "Bl" "Iz"
## [1] "and E:"
## [1] "To" "Aj" "Ma" "Pe"## Set of permutations < 'minperm'. Generating entire set.## [1] "results for grouping 4 W:"
## [1] "An" "Bl" "Ma"
## [1] "and E:"
## [1] "To" "Aj" "Iz" "Pe"## Set of permutations < 'minperm'. Generating entire set.## [1] "results for grouping 5 W:"
## [1] "An" "Bl" "Pe"
## [1] "and E:"
## [1] "To" "Aj" "Iz" "Ma"## Set of permutations < 'minperm'. Generating entire set.## [1] "results for grouping 6 W:"
## [1] "An" "To" "Aj"
## [1] "and E:"
## [1] "Bl" "Iz" "Ma" "Pe"## Set of permutations < 'minperm'. Generating entire set.## [1] "results for grouping 7 W:"
## [1] "An" "To" "Iz"
## [1] "and E:"
## [1] "Bl" "Aj" "Ma" "Pe"## Set of permutations < 'minperm'. Generating entire set.## [1] "results for grouping 8 W:"
## [1] "An" "To" "Ma"
## [1] "and E:"
## [1] "Bl" "Aj" "Iz" "Pe"## Set of permutations < 'minperm'. Generating entire set.## [1] "results for grouping 9 W:"
## [1] "An" "To" "Pe"
## [1] "and E:"
## [1] "Bl" "Aj" "Iz" "Ma"## Set of permutations < 'minperm'. Generating entire set.## [1] "results for grouping 10 W:"
## [1] "An" "Aj" "Iz"
## [1] "and E:"
## [1] "Bl" "To" "Ma" "Pe"## Set of permutations < 'minperm'. Generating entire set.## [1] "results for grouping 11 W:"
## [1] "An" "Aj" "Ma"
## [1] "and E:"
## [1] "Bl" "To" "Iz" "Pe"## Set of permutations < 'minperm'. Generating entire set.## [1] "results for grouping 12 W:"
## [1] "An" "Aj" "Pe"
## [1] "and E:"
## [1] "Bl" "To" "Iz" "Ma"## Set of permutations < 'minperm'. Generating entire set.## [1] "results for grouping 13 W:"
## [1] "An" "Iz" "Ma"
## [1] "and E:"
## [1] "Bl" "To" "Aj" "Pe"## Set of permutations < 'minperm'. Generating entire set.## [1] "results for grouping 14 W:"
## [1] "An" "Iz" "Pe"
## [1] "and E:"
## [1] "Bl" "To" "Aj" "Ma"## Set of permutations < 'minperm'. Generating entire set.## [1] "results for grouping 15 W:"
## [1] "An" "Ma" "Pe"
## [1] "and E:"
## [1] "Bl" "To" "Aj" "Iz"## Set of permutations < 'minperm'. Generating entire set.## [1] "results for grouping 16 W:"
## [1] "Bl" "To" "Aj"
## [1] "and E:"
## [1] "An" "Iz" "Ma" "Pe"## Set of permutations < 'minperm'. Generating entire set.## [1] "results for grouping 17 W:"
## [1] "Bl" "To" "Iz"
## [1] "and E:"
## [1] "An" "Aj" "Ma" "Pe"## Set of permutations < 'minperm'. Generating entire set.## [1] "results for grouping 18 W:"
## [1] "Bl" "To" "Ma"
## [1] "and E:"
## [1] "An" "Aj" "Iz" "Pe"## Set of permutations < 'minperm'. Generating entire set.## [1] "results for grouping 19 W:"
## [1] "Bl" "To" "Pe"
## [1] "and E:"
## [1] "An" "Aj" "Iz" "Ma"## Set of permutations < 'minperm'. Generating entire set.## [1] "results for grouping 20 W:"
## [1] "Bl" "Aj" "Iz"
## [1] "and E:"
## [1] "An" "To" "Ma" "Pe"## Set of permutations < 'minperm'. Generating entire set.## [1] "results for grouping 21 W:"
## [1] "Bl" "Aj" "Ma"
## [1] "and E:"
## [1] "An" "To" "Iz" "Pe"## Set of permutations < 'minperm'. Generating entire set.## [1] "results for grouping 22 W:"
## [1] "Bl" "Aj" "Pe"
## [1] "and E:"
## [1] "An" "To" "Iz" "Ma"## Set of permutations < 'minperm'. Generating entire set.## [1] "results for grouping 23 W:"
## [1] "Bl" "Iz" "Ma"
## [1] "and E:"
## [1] "An" "To" "Aj" "Pe"## Set of permutations < 'minperm'. Generating entire set.## [1] "results for grouping 24 W:"
## [1] "Bl" "Iz" "Pe"
## [1] "and E:"
## [1] "An" "To" "Aj" "Ma"## Set of permutations < 'minperm'. Generating entire set.## [1] "results for grouping 25 W:"
## [1] "Bl" "Ma" "Pe"
## [1] "and E:"
## [1] "An" "To" "Aj" "Iz"## Set of permutations < 'minperm'. Generating entire set.## [1] "results for grouping 26 W:"
## [1] "To" "Aj" "Iz"
## [1] "and E:"
## [1] "An" "Bl" "Ma" "Pe"## Set of permutations < 'minperm'. Generating entire set.## [1] "results for grouping 27 W:"
## [1] "To" "Aj" "Ma"
## [1] "and E:"
## [1] "An" "Bl" "Iz" "Pe"## Set of permutations < 'minperm'. Generating entire set.## [1] "results for grouping 28 W:"
## [1] "To" "Aj" "Pe"
## [1] "and E:"
## [1] "An" "Bl" "Iz" "Ma"## Set of permutations < 'minperm'. Generating entire set.## [1] "results for grouping 29 W:"
## [1] "To" "Iz" "Ma"
## [1] "and E:"
## [1] "An" "Bl" "Aj" "Pe"## Set of permutations < 'minperm'. Generating entire set.## [1] "results for grouping 30 W:"
## [1] "To" "Iz" "Pe"
## [1] "and E:"
## [1] "An" "Bl" "Aj" "Ma"## Set of permutations < 'minperm'. Generating entire set.## [1] "results for grouping 31 W:"
## [1] "To" "Ma" "Pe"
## [1] "and E:"
## [1] "An" "Bl" "Aj" "Iz"## Set of permutations < 'minperm'. Generating entire set.## [1] "results for grouping 32 W:"
## [1] "Aj" "Iz" "Ma"
## [1] "and E:"
## [1] "An" "Bl" "To" "Pe"## Set of permutations < 'minperm'. Generating entire set.## [1] "results for grouping 33 W:"
## [1] "Aj" "Iz" "Pe"
## [1] "and E:"
## [1] "An" "Bl" "To" "Ma"## Set of permutations < 'minperm'. Generating entire set.## [1] "results for grouping 34 W:"
## [1] "Aj" "Ma" "Pe"
## [1] "and E:"
## [1] "An" "Bl" "To" "Iz"## Set of permutations < 'minperm'. Generating entire set.## [1] "results for grouping 35 W:"
## [1] "Iz" "Ma" "Pe"
## [1] "and E:"
## [1] "An" "Bl" "To" "Aj"## Set of permutations < 'minperm'. Generating entire set.EW_results_permu<-cbind(EW_groups, permu_results)
colnames(EW_results_permu)[4]<-"p_value"
EW_results_permu